Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction.

In order to improve our understanding of cancer and develop multi-layered theoretical models for the underlying mechanism, it is essential to have enhanced understanding of the interactions between multiple levels of genomic data that contribute to tumor formation and progression. Although there exist recent approaches such as a graph-based framework that integrates multi-omics data including copy number alteration, methylation, gene expression, and miRNA data for cancer clinical outcome prediction, most of previous methods treat each genomic data as independent and the possible interplay between them is not explicitly incorporated to the model. However, cancer is dysregulated by multiple levels in the biological system through genomic, epigenomic, transcriptomic, and proteomic level. Thus, genomic features are likely to interact with other genomic features in the different genomic levels. In order to deepen our knowledge, it would be desirable to incorporate such inter-relationship information when integrating multi-omics data for cancer clinical outcome prediction. In this study, we propose a new graph-based framework that integrates not only multi-omics data but inter-relationship between them for better elucidating cancer clinical outcomes. In order to highlight the validity of the proposed framework, serous cystadenocarcinoma data from TCGA was adopted as a pilot task. The proposed model incorporating inter-relationship between different genomic features showed significantly improved performance compared to the model that does not consider inter-relationship when integrating multi-omics data. For the pair between miRNA and gene expression data, the model integrating miRNA, for example, gene expression, and inter-relationship between them with an AUC of 0.8476 (REI) outperformed the model combining miRNA and gene expression data with an AUC of 0.8404. Similar results were also obtained for other pairs between different levels of genomic data. Integration of different levels of data and inter-relationship between them can aid in extracting new biological knowledge by drawing an integrative conclusion from many pieces of information collected from diverse types of genomic data, eventually leading to more effective screening strategies and alternative therapies that may improve outcomes.

Dissociation of cellular proliferation and c-myc expression by buttercup extract.

Buttercup extract (BE), an extract of the buttercup plant (Zanthoriza simplicissima), inhibits RNA and DNA synthesis by HL-60 promyelocytic leukemia cells. Exposure of these cells to 3% BE for 48 hours results in dramatic inhibition of RNA synthesis without loss of cell viability. The effect of BE is partially reversible over 12-24 hours with the level of RNA synthesis returning nearly to control levels during this time period. DNA synthesis is also reversibly inhibited by exposure to BE. Despite the inhibition of RNA synthesis in HL-60 cells, there is no decrease in the level of c-myc mRNA, even at high BE concentrations. The level of gene-specific mRNA for the c-Ha-ras, c-fms, and c-mos genes in these cells also remained constant during exposure to BE. Ribosomal RNA is not degraded during 24 hours of BE treatment in vitro, suggesting that BE does not maintain the relative mRNA level for these genes by selective degradation of other RNA species. The inhibition of RNA and DNA synthesis by BE without a corresponding alteration in the level of expression of the c-myc gene suggests that this agent dissociates c-myc expression and cellular proliferation in these cells.